Analysis of large and dense networks based on topology is very difficult due to the computational challenges of extracting meaningful topological features from networks. In this paper, we present a computationally tra...
详细信息
ISBN:
(纸本)9783031439926;9783031439933
Analysis of large and dense networks based on topology is very difficult due to the computational challenges of extracting meaningful topological features from networks. In this paper, we present a computationally tractable approach to topological data analysis of large and dense networks. The approach utilizes principled theory from persistent homology and optimal transport to define a novel vector space representation for topological features. The feature vectors are based on persistence diagrams of connected components and cycles and are computed very efficiently. The associated vector space preserves the Wasserstein distance between persistence diagrams and fully leverages the Wasserstein stability properties. This vector space representation enables the application of a rich collection of vector-based models from statistics and machine learning to topological analyses. The effectiveness of the proposed representation is demonstrated using support vector machines to classify measured functional brain networks. Code for the topological vector space is available at https://***/topolearn.
The agricultural sector faces serious obstacles from plant diseases, which lower crop production and cause financial losses. Numerous leaf diseases can affect coconut, a beverage that is enjoyed widely worldwide. Trad...
详细信息
Approximately 962,000,000 persons worldwide are 60 or older. Despite the widespread adoption of techniques for human activity detection, there has been a dearth of study into the specific challenge of identifying the ...
详细信息
Automated game intelligence is a crucial step in rapid game development. A promising research direction for automated game intelligence is reinforcement learning, and specifically, the proximal policy optimization (PP...
详细信息
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigorous theoretical understanding of the...
详细信息
This paper proposes an open-ended task for Visual Question Answering (VQA) that leverages the InceptionV3 Object Detection model and an attention-based Long Short-Term Memory (LSTM) network for question answering. Our...
详细信息
With the increase in sharing of videos worldwide over social networks, presence of high-quality fakes is on increase. Forged videos affect the authenticity and integrity of the video as a whole. This can lead to serio...
详细信息
Time series forecasting aims to model the change in data points over time. It is applicable in many areas, such as energy consumption, solid waste generation, economic indicators (inflation, currency), global warming ...
详细信息
Most Multi-Objective Evolutionary Algorithms (MOEAs) face significant challenges in many-objective optimization. MOEAs have random elements in the selection and crossover operators which endow them with global converg...
详细信息
This paper proposes a novel preference scale function based on the Poincare metric for decision-making with Intuitionistic Fuzzy Sets (IFSs). We first introduce a pair of two-dimensional vectors expressing the IFSs in...
详细信息
暂无评论